eventdetector-ts 1.0.6

This package provides an easy-to-use and efficient solution for event detection in time series data. It includes various functionalities and tools that enable users to apply our framework to their own data sets and customize the detection process according to their specific needs.

Universal Event Detection in Time Series

Welcome to Event Detector, a Python package for detecting events in time series data. The emphasis of this package is on offering useful machine learning functionalities, such as customizing and fitting the model on multidimensional time series, training on large datasets, ensemble models, and providing rich support for event detection in time series.

Installation

Pypi installation

pip install eventdetector-ts

Manual installation

To get started using Event Detector, simply follow the instructions below to install the required packages and dependencies.

Clone the repository:

git clone https://github.com/menouarazib/eventdetector.git
cd eventdetector

Create a virtual environment:

python -m venv env
source env/bin/activate  # for Linux/MacOS
env\Scripts\activate.bat  # for Windows

Install the required packages:

pip install -r requirements.txt

Quickstart Example

Data sets

To quickly get started with the Event Detection in Time Series package, follow the steps below:

• You can either download the datasets and event catalogs manually or use the built-in methods for the desired application:
  • Martian Bow Shock: eventdetector_ts.load_martian_bow_shock()
    • bow_shock_dataset
    • bow_shock_events
  • Credit Card Fraud: eventdetector_ts.load_credit_card_fraud()
    • credit_card_dataset
    • credit_card_events

Code Implementations:

• Credit Card Fraud:

  from eventdetector_ts import load_credit_card_fraud
  from eventdetector_ts.metamodel.meta_model import MetaModel
  
  dataset, events = load_credit_card_fraud()
  meta_model = MetaModel(dataset=dataset, events=events, width=3, step=1, output_dir='fraud', batch_size=3000)
  # Prepare the events and dataset for computing op.
  meta_model.prepare_data_and_computing_op()
  # Builds a stacking learning pipeline using the provided models and hyperparameters.
  meta_model.build_stacking_learning()
  # Run the Event Extraction Optimization process.
  meta_model.event_extraction_optimization()
  # Plot the results: Losses, true/predicted op, true/predicted events, deltat_t.
  meta_model.plot_save(show_plots=True)
  

• Martian Bow Shock:

  from eventdetector_ts import load_martian_bow_shock
  from eventdetector_ts.metamodel.meta_model import MetaModel
  
  dataset, events = load_martian_bow_shock()
  
  # Create the MetaModel.
  meta_model = MetaModel(output_dir="martian_bow_shocks", dataset=dataset, events=events, width=45, step=1,
                         time_window=5400, batch_size=3000)
  # Prepare the events and dataset for computing op.
  meta_model.prepare_data_and_computing_op()
  # Builds a stacking learning pipeline using the provided models and hyperparameters.
  meta_model.build_stacking_learning()
  # Run the Event Extraction Optimization process.
  meta_model.event_extraction_optimization()
  # Plot the results: Losses, true/predicted op, true/predicted events, deltat_t.
  meta_model.plot_save(show_plots=True)
  

Results and Performance Evaluation

Performance Metrics

Table below presents the performance metrics for precision, recall, and F1-Score, providing a quantitative assessment of the framework's accuracy and effectiveness in the two data sets.

Data set	F1-Score	Precision	Recall
Martian bow shock	0.9021	0.9455	0.8626
Credit card fraud	0.8372	0.9643	0.7397

Training and Validation Losses

The Figure below showcases the training loss and validation loss of the stacked models during the training process on the Martian bow shock and credit card fraud cases. The stacked models used in this evaluation consist of two feedforward neural networks (FFN_0, FFN_1) with distinct configurations of hyperparameters. The low losses observed in both cases indicate that the meta model has successfully learned the underlying patterns, justifying the obtained good metrics.

Comparison of Predicted op and True op

The Figure below illustrates the comparison between the predicted $op$ values and the true $op$ values on the Martian bow shock (delta = 180 seconds) and credit card fraud (delta = 3 seconds) datasets.

MetaModel Arguments:

Argument	Description
output_dir	The name or path of the directory where all outputs will be saved. If output_dir is a folder name, it will create the full path.
dataset	The input dataset as pd.DataFrame.
events	The input events as a list or pd.DataFrame.
width	The width to be used for creating overalapping partitions.
step	The step size between two successive partitions.
kwargs	Optional keyword arguments:
t_max	The maximum total time related to sigma. Default: (3 * w_s) / 2).
delta	The maximum time tolerance used to determine the correspondence between a predicted event and its actual counterpart. Default: w_s.
s_h	A step parameter for the peak height threshold h. Default: 0.05.
epsilon	A small constant used to control the size of the set containing top models with the lowest MSE values. Default: 0.0002.
pa	The patience for the early stopping algorithm. Default: 5.
t_r	The ratio threshold for the early stopping algorithm. Default: 0.97.
time_window	The 'time_window' parameter is crucial for controlling the amount of data used in the dataset. It should be specified as a number of units of time. By default, it is set to None, which means that all available data will be used. However, if a value is provided, the dataset will only include a specific interval of data around each reference event. This interval consists of data from both the left and right sides of each event, with a duration equal to the specified time_window. Setting a time_window can offer several advantages, including speeding up the training process and improving the neural networks' understanding for rare events.
models	Determine the type of deep learning models and the number of instances to use. Default: [(model, 2) for model in [FFN]].
hyperparams_ffn	Specify for the FFN the maximum number of layers, the minimum and the maximum number of neurons per layer. Default: (3, 64, 256).
hyperparams_cnn	Specify for the CNN the minimum and maximum number of filters, the minimum, the maximum kernel size, and maximum number of pooling layers. Default: (16, 64, 3, 8 , 2).
hyperparams_rnn	Specify for the RNN the maximum number of RNN layers, the minimum and the maximum number of hidden units. Default: (1, 16, 128).
hyperparams_mm_network	Specify for the MetaModel network the number of layers and the number of neurons per layer. Default: (1, 32).
epochs	The number of epochs to train different models. Default: 256.
batch_size	The number of samples per gradient update. Default: 32.
fill_nan	Specifies the method to use for filling NaN values in the dataset. Supported methods are 'zeros', 'ffill', 'bfill', and 'median'. Default: "zeros".
type_training	Specify the type of training technique to use for the MetaModel. Supported techniques are 'average' and 'ffn'. Default: "average".
scaler	The type of scaler to use for preprocessing the data. Possible values are "MinMaxScaler", "StandardScaler", and "RobustScaler". Default: "StandardScaler".
use_kfold	Whether to use k-fold cross-validation technique or not. Default: False.
test_size	The proportion of the dataset to include in the test split. Should be between 0 and 1. Default is 0.2.
val_size	The proportion of the training set to use for validation. Should be a value between 0 and 1. Default is 0.2.
use_multiprocessing	Whether to use multiprocessing or not for the event exctraction optimization. The default value is False.
save_models_as_dot_format	Whether to save the models as a dot format file. The default value is False. If set to True, then you should have graphviz software to be installed on your machine.

How to credit our package

If you use our package, please cite the following paper:

@misc{azib_renard_garnier_génot_andré_2023,
 title={Universal Event Detection in Time Series},
 url={osf.io/uabjg},
 DOI={10.31219/osf.io/uabjg},
 publisher={OSF Preprints},
 author={Azib, Menouar and Renard, Benjamin and Garnier, Philippe and Génot, Vincent and André, Nicolas},
 year={2023},
 month={Jul}
}